Modern buildings are long no longer rigid concrete shells — they are complex, networked systems with thousands of sensors, actuators, and control logics. This infrastructure continuously generates large amounts of data on energy consumption, indoor climate, occupancy, and system status.
Despite this abundance of data, many optimization opportunities remain untapped. Typical causes:
Rigid control systems – classic time schedules or PID controllers respond only to current measurements, not to historical patterns or forecasts.
Growing demands – volatile energy prices, CO₂ targets, and changing usage patterns increase the pressure.
Difficult data access – relevant measurements often have to be laboriously searched for and analyzed, usually only by SQL experts.
ABB BuildingPro addresses these challenges with an integrated AI framework, which combines three key technologies:
Self-learning optimization of systems such as HVAC, lighting, or storage. The RL agent learns from live and historical data to optimally balance costs, energy, and comfort.
Reinforcement LearningPrecise forecasts for energy consumption, load peaks, indoor climate, and occupancy, to proactively control operating strategies.
ForecastingNatural-language queries instead of complex SQL commands. LLMs know all assets and measurement points and deliver results as tables, charts, or reports.
NL2SQLIn ABB BuildingPro, rule chains serve as the central orchestration platform for all AI modules:
Combine models – e.g. link forecast data with RL optimizations.
Automatic start & stop – activate AI modules only when they provide value.
Safety logic – automatically detect and stop unintended actions by AI agents.
This creates a autonomous, predictive, and secure building operation, which saves energy, smooths load peaks, and guarantees comfort – with full transparency and control.
AI orchestration in ABB BuildingProThe functions and scenarios described here show the potential of AI-supported building automation with ABB BuildingPro. Some of them are still in development or are conceived as a vision for the future. Our goal is to realize these possibilities step by step so that we can soon provide you with the full range of intelligent, self-optimizing building control.
Was this helpful?
Was this helpful?
